This paper presents our preliminary results with ABEONA, an edge-to-cloud architecture that allows migrating tasks from low-energy, resource-constrained devices on the edge up to the cloud. Our preliminary results on artificial and real world datasets show that it is possible to execute workloads in a more efficient manner energy-wise by scaling horizontally at the edge, without negatively affecting the execution runtime.
Cloud Data Centers have become the backbone infrastructure to provide services. With the emerging edge computing paradigm, computation and networking capabilities have been pushed from clouds to the edge to provide computation, intelligence, networking management with low end-to-end latency. Service migration across different computing nodes in edge and cloud computing becomes essential to guarantee the quality of service in the dynamic environment. Many studies have been conducted on the dynamic resource management involving migrating Virtual Machines to achieve various objectives, such as lo…
Cloud computing is one of the chief requirement of modern IT trade. Today's cloud industry progressively dependent on it, which lead mutually abundant solutions and challenges. Among the numerous challenges of cloud computing, cloud migration is one of the major concern, and it is necessity to design optimize solutions to advance it with time. Data migration researcher attempt to move data concerning various geographical locations, which contain huge data volumes, compact time limit and problematical architectures. Researchers aim to transfer data with minimal transmission cost and used variou…
The cloud computing literature provides various ways to utilise cloud services, each with a different viewpoint, focus, and mostly using heterogeneous technical-centric terms. This hinders efficient and consistent knowledge flow across the community. Little, if any, research has aimed on developing an integrated process model which captures core domain concepts and ties them together to provide an overarching view of migrating legacy systems to cloud platforms that is customisable for a given context. We adopt design science research guidelines in which we use a metamodeling approach to develo…
The latency issue of the cloud-centric IoT management system has motivated Fog and Edge Computing (FEC) architecture, which distributes the tasks from the cloud to the edge resources such as routers, switches or the IoT devices themselves. Specifically, mobile sensors of IoT system can also carry certain tasks for FEC. Considering the need of dynamic process migration from the mobile sensors to other resources when the mobile sensors unable to continue their tasks, the IoT system needs to provide a flexible mechanism that allows the mobile sensors dynamically migrate their tasks to the other F…
Currently, many businesses are using cloud computing to obtain an entire IT infrastructure remotely while delegating its management to a third party. The provider of this architecture ensures the operation and maintenance of the services while offering management capabilities via web consoles.These providers offer solutions based on bare metal or virtualization platforms (mainly virtual machines). Recently, a new type of virtualization-based on containerization technology has emerged. Containers can be deployed on bare metal servers or in virtual machines. The migration of virtual machines (VM…
In Software-Defined Networking (SDN)-enabled cloud data centers, live migration is a key approach used for the reallocation of Virtual Machines (VMs) in cloud services and Virtual Network Functions (VNFs) in Service Function Chaining (SFC). Using live migration methods, cloud providers can address their dynamic resource management and fault tolerance objectives without interrupting the service of users. However, in cloud data centers, performing multiple live migrations in arbitrary order can lead to service degradation. Therefore, efficient migration planning is essential to reduce the impact…
By integrating Software-Defined Networking and cloud computing, virtualized networking and computing resources can be dynamically reallocated through live migration of Virtual Machines (VMs). Dynamic resource management such as load balancing and energy-saving policies can request multiple migrations when the algorithms are triggered periodically. There exist notable research efforts in dynamic resource management that alleviate single migration overheads, such as single migration time and co-location interference while selecting the potential VMs and migration destinations. However, by neglec…
We propose throughput and cost optimal job scheduling algorithms in cloud computing platforms offering Infrastructure as a Service. We first consider online migration and propose job scheduling algorithms to minimize job migration and server running costs. We consider algorithms that assume knowledge of job-size on arrival of jobs. We characterize the optimal cost subject to system stability. We develop a drift-plus-penalty framework based algorithm that can achieve optimal cost arbitrarily closely. Specifically this algorithm yields a trade-off between delay and costs. We then relax the job-s…
Virtual Memory Streaming Technique for Virtual Machines (VMs) for Rapid Scaling and High Performance in Cloud Environment
本文讨论了虚拟内存流（VMS）技术在云环境中提供虚拟机（VM）的影响。VMS是一种可扩展的虚拟化技术，允许不同的虚拟机快速扩展，高性能，并提高硬件利用率。传统的虚拟机管理程序不支持真正的无停机实时迁移，并且其缺乏内存超额订阅可能会通过限制每个主机上的VM数量来损害私有云部署的经济性。VMS汇集了多种高级虚拟机管理程序存储管理技术，包括粒度页面共享、动态内存占用管理、实时迁移、读缓存和独特的虚拟机克隆功能。描述了一个架构模型，以及一个概念验证实现，即VMS通过真正的实时迁移和虚拟机克隆来动态扩展虚拟化基础架构。本文认为，VMS for Cloud允许显著减少服务器内存，并通过立即添加更多虚拟机来减少虚拟化资源扩展的时间。
The containerized services allocated in the mobile edge clouds bring up the opportunity for large-scale and real-time applications to have low latency responses. Meanwhile, live container migration is introduced to support dynamic resource management and users' mobility. However, with the expansion of network topology scale and increasing migration requests, the current multiple migration planning and scheduling algorithms of cloud data centers can not suit large-scale scenarios in edge computing. The user mobility-induced live migrations in edge computing require near real-time level scheduli…
Interactive computing notebooks, such as Jupyter notebooks, have become a popular tool for developing and improving data-driven models. Such notebooks tend to be executed either in the user's own machine or in a cloud environment, having drawbacks and benefits in both approaches. This paper presents a solution developed as a Jupyter extension that automatically selects which cells, as well as in which scenarios, such cells should be migrated to a more suitable platform for execution. We describe how we reduce the execution state of the notebook to decrease migration time and we explore the kno…
The pervasive availability of streaming data is driving interest in distributed Fast Data platforms for streaming applications. Such latency-sensitive applications need to respond to dynamism in the input rates and task behavior using scale-in and -out on elastic Cloud resources. Platforms like Apache Storm do not provide robust capabilities for responding to such dynamism and for rapid task migration across VMs. We propose several dataflow checkpoint and migration approaches that allow a running streaming dataflow to migrate, without any loss of in-flight messages or their internal tasks stat…
Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long battery life, and heavy computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this paper, we analyze the effect of platform-dependent native applicatio…
Load balancing is vital for the efficient and long-term operation of cloud data centers. With virtualization, post (reactive) migration of virtual machines after allocation is the traditional way for load balancing and consolidation. However, reactive migration is not easy to obtain predefined load balance objectives and may interrupt services and bring instability. Therefore, we provide a new approach, called Prepartition, for load balancing. It partitions a VM request into a few sub-requests sequentially with start time, end time and capacity demands, and treats each sub-request as a regular…
The rapid cloud computing growth has turned data center energy consumption into a global problem. At the same time, modern cloud providers operate multiple geographically-distributed data centers. Distributed data center infrastructure changes the rules of cloud control, as energy costs depend on current regional electricity prices and temperatures. Furthermore, to account for emerging technologies surrounding the cloud ecosystem, a maintainable control solution needs to be forward-compatible. Existing cloud controllers are focused on VM consolidation methods suitable only for a single data ce…
In this paper, a re-evaluation undertaken for dynamic VM consolidation problem and optimal online deterministic algorithms for the single VM migration in an experimental environment. We proceeded to focus on energy and performance trade-off by planet lab workload traces, which consists of a thousand Planetlab VMs with widespread simulation environments. All experiments are done in a simulated cloud environment by the CloudSim simulation tool. A new paradigm of utility-oriented IT services is cloud computing, which offers a pay-as-you-go model. In recent years, there has been increasing interes…
Smart devices have become an indispensable part of our lives and gain increasing applicability in almost every area. Latency-aware applications such as Augmented Reality (AR), autonomous driving, and online gaming demand more resources such as network bandwidth and computational capabilities. Since the traditional mobile networks cannot fulfill the required bandwidth and latency, Mobile Edge Cloud (MEC) emerged to provide cloud computing capabilities in the proximity of users on 5G networks. In this paper, we consider a heterogeneous MEC network with numerous mobile users that send their tasks…
In mobile edge computing, local edge servers can host cloud-based services, which reduces network overhead and latency but requires service migrations as users move to new locations. It is challenging to make migration decisions optimally because of the uncertainty in such a dynamic cloud environment. In this paper, we formulate the service migration problem as a Markov Decision Process (MDP). Our formulation captures general cost models and provides a mathematical framework to design optimal service migration policies. In order to overcome the complexity associated with computing the optimal …
An SMDP-based Resource Management Scheme for Distributed Cloud Systems
本文研究了地理分布云系统中的资源管理问题。采用Follow Me Cloud概念，实现跨联合数据中心（DC）的服务迁移。因此，有两种类型的服务请求到DC，即，在本地服务区域中发起的新请求（NR）和当移动用户跨服务区域移动时生成的迁移请求（MR）。提出了一种新的资源管理方案，帮助资源管理器决定是否接受服务请求（NR或MR），并确定多少资源应分配给每个服务（如果接受）。优化目标是最大化系统平均回报，并保持服务请求的拒绝概率在一定的阈值之内。数值结果表明，与其他资源管理方案相比，该方案能显著提高系统的整体效用和用户体验.
As a key technology in the 5G era, Mobile Edge Computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service (QoS). There…
Ayandegan Institute of Higher Education（阿扬德甘高等教育学院）；Islamic Azad University（伊斯兰阿扎德大学）
Cloud computing service models have experienced rapid growth and inefficient resource usage is known as one of the greatest causes of high energy consumption in cloud data centers. Resource allocation in cloud data centers aiming to reduce energy consumption has been conducted using live migration of Virtual Machines (VMs) and their consolidation into the small number of Physical Machines (PMs). However, the selection of the appropriate VM for migration is an important challenge. To solve this issue, VMs can be classified according to the pattern of user requests into sensitive or insensitive …
In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects app…
Currently, due to the advantages of light weight, simple deployment, multi-environment support, short startup time, scalability, and easy migration, container technology has been widely used in both cloud and edge/fog computing, and addresses the problem of device heterogeneity in different computing environments. On this basis, as one of the most popular container orchestration and management systems, Kubernetes almost dominates the cloud environment. However, since it is primarily designed for centralized resource management scenarios where computing resources are sufficient, the system is u…
Saving energy is an important issue for cloud providers to reduce energy cost in a data center. With the increasing popularity of cloud computing, it is time to examine various energy reduction methods for which energy consumption could be reduced and lead us to green cloud computing. In this paper, our aim is to propose a virtual machine selection algorithm to improve the energy efficiency of a cloud data center. We are also presenting experimental results of the proposed algorithm in a cloud computing based simulation environment. The proposed algorithm dynamically took the virtual machines'…
