S-memV: Split Migration of Large-Memory Virtual Machines in IaaS Clouds

Recently, Infrastructure-as-a-Service clouds provide virtual machines (VMs) with a large amount of memory. Such large-memory VMs make VM migration difficult because it is costly to reserve large-memory hosts as the destination. Using virtual memory is a remedy for this problem, but virtual memory is incompatible with the memory access pattern in VM migration. Consequently, large performance degradation occurs during and after VM migration due to excessive paging. This paper proposes split migration of large-memory VMs with S-memV. Split migration migrates a VM to one main host and one or more sub-hosts. It divides the memory of a VM and transfers memory likely to be accessed to the main host. Since it transfers the rest of the memory directly to the sub-hosts, no paging occurs during VM migration. After split migration, remote paging is performed between the main host and the sub-hosts, but its frequency is lower thanks to memory splitting that is aware of remote paging. We have implemented S-memV in KVM and showed that the performance of split migration and application performance after VM migration were comparable to that of traditional VM migration with sufficient memory.IEEE International Conference on Cloud Computing (IEEE Cloud 2018), July 2-7, 2018, San Francisco, CA, US

Flexible and Efficient Partial Migration of Split-memory VMs

Recently, virtual machines (VMs) with a large amount of memory are being widely used. For flexible migration of such large-memory VMs without large hosts, split migration has been proposed. It transfers VM fragments to multiple smaller hosts and runs a split-memory VM across those hosts with remote paging. However, the traditional method cannot migrate a split-memory VM efficiently because it always migrates the entire VM. In addition, it has to gather all the VM fragments to one host and transfer them from that host. To address these issues, this paper proposes flexible and efficient partial migration of split-memory VMs. In particular, subst migration migrates only part of a split-memory VM to enable the maintenance of some of the hosts running the VM. Merge migration efficiently consolidates VM fragments distributed across multiple hosts into one host by directly transferring a VM fragment from each host. Even if a split-memory VM itself causes remote paging during such partial migration, the consistency of the VM is maintained by retransferring and invalidating target memory. We have implemented partial migration in KVM and showed its efficiency.2020 IEEE 13th International Conference on Cloud Computing (CLOUD 2020), 18–24 October, 2020, Beijing, China（オンライン開催に変更

Optimized Memory Encryption for VMs Across Multiple Hosts

Recently, virtual machines (VMs) with a large amount of memory are widely used. It is often not easy to migrate such a large-memory VM because VM migration requires one large destination host. To address this issue, split migration divides the memory of a VM into small pieces and transfers them to multiple destination hosts. The migrated VM exchanges its memory data between the hosts using remote paging. To prevent information leakage from the memory data in an untrusted environment, memory encryption can be used. However, encryption overhead largely affects the performance of the hosts and the VM. This paper proposes SEmigrate for optimizing the memory encryption in split migration and remote paging. SEmigrate avoids decrypting memory data at most of the destination hosts to reduce the overhead and completely prevent information leakage. Also, it selectively encrypts only the memory data containing sensitive information by analyzing the memory of the guest operating system in a VM. SEmigrate could reduce the CPU utilization during encrypted split migration by 6–20% point and improve the performance of the migrated VM with encrypted remote paging to 1.9 × .13th International Conference on Intelligent Networking and Collaborative Systems (INCoS-2021), September 1-3, 2021, Asia University, Virtual Conference (Online Presentation

Optimizing VMs across Multiple Hosts with Transparent and Consistent Tracking of Unused Memory

Recently, Infrastructure-as-a-Service (IaaS) clouds provide virtual machines (VMs) with a large amount of memory. To make the migration of such large-memory VMs flexible, split migration has been proposed. It divides the memory of a VM into smaller pieces and transfers them to multiple destination hosts. After the migration, the VM runs across multiple hosts and its memory data is exchanged between hosts by remote paging. There is often unused memory in a large-memory VM, but data of even unused memory is transferred via the network. This paper proposes FCtrans to achieve efficient split migration and remote paging by considering unused memory. FCtrans avoids transferring data of unused memory to destination hosts on split migration. Similarly, it does not perform remote paging for unused memory and immediately continues the execution of the VM. To enable this, FCtrans keeps track of the memory usage of a VM after starting split migration. In addition, it transparently and consistently reclaims memory released after used by the guest operating system using VM introspection and deals with it as unused. We have implemented FCtrans in KVM and conducted experiments using a VM with 352 GB of memory on the StarBED testbed. It is shown that split migration became up to 29x faster and the memory access performance of a VM across multiple hosts improved by up to 85 %.2021 IEEE 14th International Conference on Cloud Computing (CLOUD), September 5-10, 2021, Chicago, IL, US

Transparent IDS Offloading for Split-Memory Virtual Machines

To enable virtual machines (VMs) with a large amount of memory to be flexibly migrated, split migration has been proposed. It divides a large-memory VM into small pieces and transfers them to multiple hosts. After the migration, the VM runs across those hosts and exchanges memory data between hosts using remote paging. For such a split-memory VM, however, it becomes difficult to securely run intrusion detection systems (IDS) outside the VM using a technique called IDS offloading. This paper proposes VMemTrans to support transparent IDS offloading for split-memory VMs. In VMemTrans, offloaded IDS can monitor a split-memory VM as if that memory were not distributed. To achieve this, VMemTrans enables IDS running in one host to transparently access VM\u27s remote memory. To consider a trade-off, it provides two methods for obtaining memory data from remote hosts: self paging and proxy paging. We have implemented VMemTrans in KVM and compared the execution performance between the two methods.2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), 13-17 July 2020, Madrid, Spain（新型コロナ感染拡大に伴い、現地開催中止

Efficient and Flexible Checkpoint/Restore of Split-memory Virtual Machines

Recently, clouds provide virtual machines (VMs) with a large amount of memory for big data analysis. For easier migration of such VMs, split migration divides the memory of a VM into several pieces and transfers them to multiple hosts. Since the migrated VM called a split-memory VM needs to exchange memory data between the hosts, it is inherently subject to host and network failures. As a countermeasure, a checkpoint/restore mechanism has been used to periodically save the state of a VM, but the traditional mechanism is not suitable for split-memory VMs. It has to move a large amount of memory data between hosts during checkpointing and can just restores a normal VM on one host. This paper proposes D-CRES for efficient and flexible checkpoint/restore of split-memory VMs. D-CRES achieves fast checkpointing by saving the memory of a VM in parallel at all the hosts without moving memory data. For live checkpointing, it consistently saves the memory of a running VM by considering memory data exchanged by the VM itself. In addition, it enables a split-memory VM to be restored in parallel at multiple hosts. We have implemented checkpoint/restore of D-CRES in KVM and showed that the performance was up to 5.4 times higher than that of using the traditional mechanism.2020 International Conference on Computational Intelligence (ICCI), 8-9 October, 2020, Universiti Teknologi PETRONAS (UTP), Bandar Seri Iskandar, Malaysia（オンライン開催に変更

A Survey on Load Balancing Algorithms for VM Placement in Cloud Computing

The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure and be charged on pay-per-use basis. However, Cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements (SLAs) violations. To achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be NP-hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to PMs in infrastructure Clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.Comment: 22 Pages, 4 Figures, 4 Tables, in pres