A comprehensive survey for scheduling techniques in cloud computing

Workload prediction in cloud using artificial neural network and adaptive differential evolution

Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area.

Auto-Scaling Web Applications in Clouds: A Taxonomy and Survey

Survey on prediction models of applications for resources provisioning in cloud

The massive proliferation of wireless infrastructures with complementary characteristics prompts the bandwidth aggregation for Concurrent Multipath Transfer (CMT) over heterogeneous access networks. Stream Control Transmission Protocol (SCTP) is the standard transport-layer solution to enable CMT in multihomed communication environments. However, delivering high-quality streaming video with the existing CMT solutions still remains problematic due to the stringent QoS (Quality of Service) requirements and path asymmetry in heterogeneous wireless networks. In this paper, we advance the state of the art by introducing video distortion into the decision process of multipath data transfer. The proposed Distortion-Aware Concurrent Multipath Transfer (CMT-DA) solution includes three phases: 1) per-path status estimation and congestion control; 2) quality-optimal video flow rate allocation; 3) delay and loss controlled data retransmission. The term `flow rate allocation' indicates dynamically picking appropriate access networks and assigning the transmission rates. We analytically formulate the data distribution over multiple communication paths to minimize the end-to-end video distortion and derive the solution based on the utility maximization theory. The performance of the proposed CMT-DA is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that CMT-DA outperforms the reference schemes in terms of video PSNR (Peak Signal-to-Noise Ratio), goodput, and inter-packet delay.

Distortion-Aware Concurrent Multipath Transfer for Mobile Video Streaming in Heterogeneous Wireless Networks

Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.

A cost-aware auto-scaling approach using the workload prediction in service clouds

Service platforms have disadvantages such as they have long construction periods, low resource utilizations and isolated constructions. Migrating service platforms into clouds can solve these problems. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The automatic scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user SLA while keeping scaling costs low.

Workload Predicting-Based Automatic Scaling in Service Clouds

We consider the problem of high definition video transmission in a heterogeneous wireless network from a video server to a multihomed client. On one hand, a single wireless network is limited in transmission performance (e.g., available bandwidth and link delay); On the other hand, the high definition video streaming is characterized by high transmission rate and large-size video frames. Thus, the end-to-end video frame delay becomes a severely challenging problem which is critical for real-time video applications. In this paper, we propose a novel scheduling approach named SFL, which deliberately splits large-size video frames into sub-frames and dispatches each of them onto a different wireless network to the multihomed client. This approach is able to improve the frame-level delay performance for enhancing video quality. We formulate a optimization problem of video streaming allocation for minimizing the end-to-end delay based on the network calculus and derive its solution with the water filling algorithm. We evaluate the performance of the proposed SFL through Exata emulations using real-time H.264 video streaming. Emulation results show that SFL outperforms existing frame level scheduling approaches in improving frame-level delay as well as in enhancing video quality in terms of PSNR (Peak Signal-to-Noise Ratio).

A low latency scheduling approach for high definition video streaming over heterogeneous wireless networks

Load distribution is vital to the performance of multipath transport. The task becomes more challenging in real-time multimedia applications (RTMA), which impose stringent delay requirements. Two key issues to be addressed are: 1) How to minimize end-to-end delay and 2) how to alleviate packet reordering that incurs additional recovery time at the receiver. In this paper, we propose sub-packet based multipath load distribution (SPMLD), a new model that splits traffic at the granularity of sub-packet. Our SPMLD model aims to minimize total packet delay by effectively aggregating multiple parallel paths as a single virtual path. First, we formulate the packet splitting over multiple paths as a constrained optimization problem and derive its solution based on progressive approximation method. Second, in the solution, we analyze queuing delay by introducing D/M/1 model and obtain the expression of dynamic packet splitting ratio for each path. Third, in order to describe SPMLD's scheduling policy, we propose two distributed algorithms respectively implemented in the source and destination nodes. We evaluate the performance of SPMLD through extensive simulations in QualNet using real-time H.264 video streaming. Experimental results demonstrate that: SPMLD outperforms previous flow and packet based load distribution models in terms of video peak signal-to-noise ratio, total packet delay, end-to-end delay, and risk of packet reordering. Besides, SPMLD's extra overhead is tiny compared to the input video streaming.

SPMLD: Sub-packet based multipath load distribution for real-time multimedia traffic

As cloud-based services become more numerous and dynamic, service deployment becomes more and more challenging. For the reason of economic, the service deployment strategy is crucial for maximize the system utility and profit gained. In this paper, considered both the cost and quality, the service deployment in cloud is modeled as a congestion game, where every service has several components is considered as a player and his strategy is to select the subset of resources to deploy his components. Based on the congestion game, a game theoretic method is proposed to optimize both the overall cost and quality. To solve the congestion game, efficient algorithms are proposed to achieve the equilibrium in polynomial time.

Jingqi Yang

Papers

A cost-aware auto-scaling approach using the workload prediction in service clouds

Workload Predicting-Based Automatic Scaling in Service Clouds

A low latency scheduling approach for high definition video streaming over heterogeneous wireless networks

SPMLD: Sub-packet based multipath load distribution for real-time multimedia traffic

A Game Theory of Cloud Service Deployment