You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Survey of Memory Management Techniques for HPC and Cloud Computing 
Chapter Title:  J. VM MIGRATION 
1. Abstract of Cited Paper (BIB001):  Abstract Cloud computing is an emerging computing technology for large data centers that maintains computational resources through the internet, rather than on local computers. With the increasing popularity of cloud computing, also increase the demand of cloud resources. In Infrastructure-as-a-Service datacenters, the placements of Virtual Machines (VMs) on physical hosts are dynamically optimized in response to resource utilization of the hosts. VM migration provides the capability to balance the load, system maintenance and fault tolerance etc. However, existing migration techniques, used to migrate virtual machines keeping data images of VMs in host and skipping transfer of unchanged data fields to reduce the amount of transfer data during migration, if number of migrations increases, number of data images stored on host are also increased, this causes memory starvation. In this paper, we propose a technique that reduces the size of data image stored on source host before migration. When a Virtual Machine migrates to another host, the data image for that VM is kept in the source host after removing unwanted data according to the probability factor. When the VM migrates back to the original host later, the kept memory image will be “reused”, i.e. data which are identical to the kept data will not be transferred and comparative to existing system the size of memory image is small. To validate this approach, results evaluated using different threshold levels and probability factor of change in data. Proposed system required less memory to store the memory image and allow more VMs to be hosted. Specifically, our proposed work is used to improve resource efficiency throughout by reducing the size of memory image that is stored on source host. Evaluations show that size of memory image reduced 33% (approx) of unnecessary memory consumption. 
2. Abstract of Cited Paper (BIB002):  Dynamic Virtual Machine (VM) consolidation is one of the most promising solutions to reduce energy consumption and improve resource utilization in data centers. Since VM consolidation problem is strictly NP-hard, many heuristic algorithms have been proposed to tackle the problem. However, most of the existing works deal only with minimizing the number of hosts based on their current resource utilization and these works do not explore the future resource requirements. Therefore, unnecessary VM migrations are generated and the rate of Service Level Agreement (SLA) violations are increased in data centers. To address this problem, our VM consolidation method which is formulated as a bin-packing problem considers both the current and future utilization of resources. The future utilization of resources is accurately predicted using a k-nearest neighbor regression based model. In this paper, we investigate the effectiveness of VM and host resource utilization predictions in the VM consolidation task using real workload traces. The experimental results show that our approach provides substantial improvement over other heuristic algorithms in reducing energy consumption, number of VM migrations and number of SLA violations. 
3. Abstract of Cited Paper (BIB003):  Host-side flash caching has emerged as a promising solution to the scalability problem of virtual machine (VM) storage in cloud computing systems, but it still faces serious limitations in capacity and endurance. This paper presents CloudCache, an on-demand cache management solution to meet VM cache demands and minimize cache wear-out. First, to support on-demand cache allocation, the paper proposes a new cache demand model, Reuse Working Set (RWS), to capture only the data with good temporal locality, and uses the RWS size (RWSS) to model a workload's cache demand. By predicting the RWSS online and admitting only RWS into the cache, CloudCache satisfies the workload's actual cache demand and minimizes the induced wear-out. Second, to handle situations where a cache is insufficient for the VMs' demands, the paper proposes a dynamic cache migration approach to balance cache load across hosts by live migrating cached data along with the VMs. It includes both on-demand migration of dirty data and background migration of RWS to optimize the performance of the migrating VM. It also supports rate limiting on the cache data transfer to limit the impact to the co-hosted VMs. Finally, the paper presents comprehensive experimental evaluations using real-world traces to demonstrate the effectiveness of CloudCache. 
4. Abstract of Cited Paper (BIB004):  Cloud computing is growing in popularity among computing paradigms, but in practice the energy consumption of the cloud data centers is very high. In this paper, we build the resource model of the cloud data center and the dynamic power model of the physical machine, and then propose a three-dimensional virtual resource scheduling method for energy saving in cloud computing (TVRSM), in which the process of virtual resource scheduling are divided into three stages: virtual resource allocation, virtual resource scheduling and virtual resource optimization. For the different objective of each stage, we design three different algorithms, respectively. The simulation results prove that the TVRSM is able to efficiently allocate and manage the virtual resources in the cloud data center. And compared with other traditional algorithms, the TVRSM can effectively reduce the energy consumption of the cloud data center and significantly minimize the amount of violations of Service Level Agreement (SLA). We propose a three-dimensional virtual resource (VR) scheduling method.We present a bin packing problem based heuristics VR allocation algorithm.We demonstrate a multi-dimensional power-aware based VR scheduling algorithm.We put forward a VR optimization algorithm to reduce energy consumption.The simulation results demonstrate the effectiveness of the proposed method. 
5. Abstract of Cited Paper (BIB005):  In recent years, power consumption has become one of the hottest research trends in computer science and industry. Most of the reasons are related to the operational budget and the environmental issues. In this paper, we would like to propose an energy-efficient solution for orchestrating the resource in cloud computing. In nature, the proposed approach firstly predicts the resource utilization of the upcoming period based on the Gaussian process regression method. Subsequently, the convex optimization technique is engaged to compute an appropriate quantity of physical servers for each monitoring window. This quantity of interest is calculated to ensure that a minimum number of servers can still provide an acceptable quality of service. Finally, a corresponding migrating instruction is issued to stack the virtual machines and turn off the idle physical servers to achieve the objective of energy savings. In order to evaluate the proposed method, we conduct the experiments using synthetic data from 29-day period of Google traces and real workload from the Montage open-source toolkit. Through the evaluation, we show that the proposed approach can achieve a significant result in reducing the energy consumption as well as maintaining the system performance. Balance between energy efficiency and quality of service in the cloud computing.Apply prediction technique to enhance the usefulness of monitoring statistics.Design optimal energy efficiency architecture to orchestrate the cloud system.After VM consolidation, adaptively turn-off idle physical machines to save energy. 
6. Abstract of Cited Paper (BIB006):  Abstract Optimizing the virtual machine (VM) migration is an important issue of server consolidation in the cloud data center. By leveraging the content similarity among the memory of VMs, the time and the amount of transferred data in VM migration, as well as the pressure of network traffic, can be reduced. There are two problems in server consolidation: (1) determining which VMs should be migrated from the overloaded hosts (VM selection problem) and (2) how to place these VMs to the destination hosts (VM placement problem). By exploiting the content similarity, we redefine the above two problems into one problem to minimize the transferred memory data in VM migration. Given a fixed host overloaded threshold, an approximation algorithm is proposed to solve the problem with one overloaded host and one destination host. For the case of multiple overloaded hosts and destination hosts, two heuristic algorithms are presented with fixed and dynamic overloaded threshold respectively. We conduct a real workload trace based simulation to evaluate the performance of our algorithms. The result shows that our algorithms can produce fewer transferred VM memory data and consume less energy than existing policies. 
7. Abstract of Cited Paper (BIB007):  Abstract Internet of Things (IoT) is a promising paradigm enabling many applications to network together through the internet. A huge volume of data is generated by such IoT applications for computation, storage, and analytics through the infrastructure and platform as service offered by cloud computing. Placement and execution of IoT applications in the cloud is a challenging task. In cloud-based IOT application, sudden changes in the sensing environment cause spikes of data flowing into the cloud. This causes resource starvation in the virtual machine and initiates migration of virtual machine from one physical server to other. However, unplanned migration causes a severe performance degradation to the application running on the cloud. Selection of suitable destination server for the virtual machine during migration is an important concern. This paper proposes a resource-aware virtual machine migration technique. Any sudden change in the sensing environment is observed by clustering the servers. The suitable target server is selected based on the resource utilization and job arrival rate of the destination server. The proposed technique is implemented in cloud platform running analytics on smart agriculture application. The evaluation results show that the proposed method outperforms the state of art techniques in terms of the number of migrations, energy utilization and migration time. 
8. Abstract of Cited Paper (BIB008):  When deploying memory-intensive applications to public clouds, one important yet challenging problem is selecting a specific instance type whose memory capacity is large enough to prevent out-of-memory errors while the cost is minimized without violating performance requirements. The state-of-the-practice solution is trial and error, causing both performance overhead and additional monetary cost. This paper investigates two memory scaling mechanisms in public cloud: physical memory (good performance and high cost) and virtual memory (degraded performance and no additional cost). In order to analyze the trade-off between performance and cost of the two scaling options, a performance-cost model is developed that is driven by a lightweight analytic prediction approach through a compact representation of the memory footprint. In addition, for those scenarios when the footprint is unavailable, a meta-model based prediction method is proposed using just-in-time migration mechanisms. The proposed techniques have been extensively evaluated with various benchmarks and real-world applications on Amazon Web Services: the performance-cost model is highly accurate with errors ranging from 1% to 4% and the proposed just-in-time migration approach reduces the monetary cost by up to 66%.

Literature Review:
Emphasis on memory is also present in works related to VM migration. A general scheme of the VM migration is shown in Figure 25 . Along with the problems with overloading and underloading PM detection where the memory utilisation rate can be considered, some approaches for the migrating VM selection analyse the memory similarity. By using the content similarity among the memory of VMs, the time and the network traffic during VM migration can be reduced. To minimise the amount of transferred memory data, research presented in BIB007 is based on Memory Buddies, a memory sharing-aware placement system for VM. Content similarity checking method with O(1) time complexity is used to evaluate the page sharing potential between VMs. In BIB005 algorithm for selecting new hosts for the VMs to be migrated is the same with the VM placement strategy. Multidimensional overload detection of nodes is used for detecting VOLUME 7, 2019 the overload nodes. It analyses the weight and current use of the CPU, memory, and network bandwidth. The approach presented in BIB006 selects the node to be migrated as the node with the lowest CPU and memory utilisation. The node with the highest estimation of utilisation and the blocking rate, which is evaluated based on the queuing theory, is determined by the power management component as the destination for VM migration. In the resource-aware VM migration technique proposed in BIB008 migration controller maintains the cluster table with all servers clustered into eight clusters based on the CPU, memory utilisation, and job arrival rate. The overloaded servers are identified using preference value. The VMs from the server with high preference values are migrated to the destination server using a resource-aware virtual machine migration technique. In traditional VM migrations, the whole data used to be transferred from source host to the destination host. In BIB002 , a technique that reduces data to be sent back to the source host was proposed. According to the reusable memory approach, the memory image on the source host is not deleted after migration. Once the VM migrates back to the original host, only processed data and not the whole data are sent back to the source host. In order to reduce the size of memory image that is stored on the source host, unnecessary data are deleted according to a probability factor. To handle cache overload situations, Cloud-Cache BIB004 provides a live VM migration with its cached data to meet the cache demands of the VMs. The approach presented in BIB009 assumes the application is migrated between different VMs without physical data movements aiming at reducing the overall monetary cost in face of memory depletion. Introducing swap for highly memory-intensive application on a memory-restrained instance was recognised as the cause of the significant application slowdown and, consequently, of the absence of lower per-hour prices. Based on the lightweight prediction methodology of the memory usage, application migrates only when the predicted memory usage exceeds the physical allocation. It migrates to the least expensive instance type that has a memory capacity larger than the current running instance. This protocol is optimistic and it shows that doubling the memory capacity could satisfy the application's memory requirement. Pacer's techniques BIB001 are based on lightweight runtime system and workload measurements, analytic models for progress predictions, and online adaptation to achieve user-defined migration objectives. The progress prediction is used to realise the best-effort migration time control and to coordinated migration of VMs. Utilisation prediction-aware best fit decreasing approach presented in BIB003 allows to migrate VMs from physical hosts that are currently overloaded or predicted to become overloaded in the near future. The live migration advantage is that the system is not turned off; consequently, there is no downtime. Nevertheless, live migration is time-consuming and challenging to perform. On the other hand, offline migration has an advantage in speed, but the host has to be turned off during migration. Whenever a VM is prepared to move from one host to another, its current state is stored in the source host for future transfer and use on the target host. In order to reduce migration time and network overhead, resent studies are focused on reducing the memory image size and cache migration policies. However, the memory migration cost, in many cases, exceeds the gain received from the migration. Surveyed research on MM in Cloud was summarised with respect to the evaluated outcome in Table 4 and the previously mentioned techniques in Table 5 .