A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed Computing (ICDCS 2017

A Priority-based Fair Queuing (PFQ) Model for Wireless Healthcare System

Healthcare is a very active research area, primarily due to the increase in the elderly population that leads to increasing number of emergency situations that require urgent actions. In recent years some of wireless networked medical devices were equipped with different sensors to measure and report on vital signs of patient remotely. The most important sensors are Heart Beat Rate (ECG), Pressure and Glucose sensors. However, the strict requirements and real-time nature of medical applications dictate the extreme importance and need for appropriate Quality of Service (QoS), fast and accurate delivery of a patient’s measurements in reliable e-Health ecosystem. As the elderly age and older adult population is increasing (65 years and above) due to the advancement in medicine and medical care in the last two decades; high QoS and reliable e-health ecosystem has become a major challenge in Healthcare especially for patients who require continuous monitoring and attention. Nevertheless, predictions have indicated that elderly population will be approximately 2 billion in developing countries by 2050 where availability of medical staff shall be unable to cope with this growth and emergency cases that need immediate intervention. On the other side, limitations in communication networks capacity, congestions and the humongous increase of devices, applications and IOT using the available communication networks add extra layer of challenges on E-health ecosystem such as time constraints, quality of measurements and signals reaching healthcare centres. Hence this research has tackled the delay and jitter parameters in E-health M2M wireless communication and succeeded in reducing them in comparison to current available models. The novelty of this research has succeeded in developing a new Priority Queuing model ‘’Priority Based-Fair Queuing’’ (PFQ) where a new priority level and concept of ‘’Patient’s Health Record’’ (PHR) has been developed and integrated with the Priority Parameters (PP) values of each sensor to add a second level of priority. The results and data analysis performed on the PFQ model under different scenarios simulating real M2M E-health environment have revealed that the PFQ has outperformed the results obtained from simulating the widely used current models such as First in First Out (FIFO) and Weight Fair Queuing (WFQ). PFQ model has improved transmission of ECG sensor data by decreasing delay and jitter in emergency cases by 83.32% and 75.88% respectively in comparison to FIFO and 46.65% and 60.13% with respect to WFQ model. Similarly, in pressure sensor the improvements were 82.41% and 71.5% and 68.43% and 73.36% in comparison to FIFO and WFQ respectively. Data transmission were also improved in the Glucose sensor by 80.85% and 64.7% and 92.1% and 83.17% in comparison to FIFO and WFQ respectively. However, non-emergency cases data transmission using PFQ model was negatively impacted and scored higher rates than FIFO and WFQ since PFQ tends to give higher priority to emergency cases. Thus, a derivative from the PFQ model has been developed to create a new version namely “Priority Based-Fair Queuing-Tolerated Delay” (PFQ-TD) to balance the data transmission between emergency and non-emergency cases where tolerated delay in emergency cases has been considered. PFQ-TD has succeeded in balancing fairly this issue and reducing the total average delay and jitter of emergency and non-emergency cases in all sensors and keep them within the acceptable allowable standards. PFQ-TD has improved the overall average delay and jitter in emergency and non-emergency cases among all sensors by 41% and 84% respectively in comparison to PFQ model

Scheduling for next generation WLANs: filling the gap between offered and observed data rates

In wireless networks, opportunistic scheduling is used to increase system throughput by exploiting multi-user diversity. Although recent advances have increased physical layer data rates supported in wireless local area networks (WLANs), actual throughput realized are significantly lower due to overhead. Accordingly, the frame aggregation concept is used in next generation WLANs to improve efficiency. However, with frame aggregation, traditional opportunistic schemes are no longer optimal. In this paper, we propose schedulers that take queue and channel conditions into account jointly, to maximize throughput observed at the users for next generation WLANs. We also extend this work to design two schedulers that perform block scheduling for maximizing network throughput over multiple transmission sequences. For these schedulers, which make decisions over long time durations, we model the system using queueing theory and determine users' temporal access proportions according to this model. Through detailed simulations, we show that all our proposed algorithms offer significant throughput improvement, better fairness, and much lower delay compared with traditional opportunistic schedulers, facilitating the practical use of the evolving standard for next generation wireless networks

Design and stability analysis of high performance packet switches

With the rapid development of optical interconnection technology, high-performance packet switches are required to resolve contentions in a fast manner to satisfy the demand for high throughput and high speed rates. Combined input-crosspoint buffered (CICB) switches are an alternative to input-buffered (IB) packet switches to provide high-performance switching and to relax arbitration timing for packet switches with high-speed ports. A maximum weight matching (MWM) scheme can provide 100% throughput under admissible traffic for lB switches. However, the high complexity of MWM prohibits its implementation in high-speed switches. In this dissertation, a feedback-based arbitration scheme for CICB switches is studied, where cell selection is based on the provided service to virtual output queues (VOQs). The feedback-based scheme is named round-robin with adaptable frame size (RR-AF) arbitration. The frame size in RR-AF is adaptably changed by the serviced and unserviced traffic. If a switch is stable, the switch provides 100% throughput. Here, it is proved that RR-AF can achieve 100% throughput under uniform admissible traffic. Switches with crosspoint buffers need to consider the transmission delays, or round-trip times to define the crosspoint buffer size. As the buffered crossbar switch can be physically located far from the input ports, actual round-trip times can be non-negligible. To support non-negligible round-trip times in a buffered crossbar switch, the crosspoint buffer size needs to be increased. To satisfy this demand, this dissertation investigates how to select the crosspoint buffer size under non-negligible round trip times and under uniform traffic. With the analysis of stability margin, the relationship between the crosspoint buffer size and round-trip time is derived. Considering that CICB switches deliver higher performance than lB switches and require no speedup, this dissertation investigates the maximum throughput performance that these switches can achieve. It is shown that CICB switches without speedup achieve 100% throughput under any admissible traffic through a fluid model. In addition, a new hybrid scheme, based on longest queue-first (as input arbitration) and longest column occupancy first (as output arbitration) is proposed, which achieves 100% throughput under uniform and non-uniform traffic patterns. In order to give a better insight of the feedback nature of arbitration scheme for CICB switches, a frame-based round-robin arbitration scheme with explicit feedback control (FRE) is introduced. FRE dynamically sets the frame size according to the input load and to the accumulation of cells in a VOQ. FRE is used as the input arbitration scheme and it is combined with RR, PRR, and FRE as output arbitration schemes. These combined schemes deliver high performance under uniform and nonuniform traffic models using a buffered crossbar with one-cell crosspoint buffers. The novelty of FRE lies in that each VOQ sets the frame size by an adjustable parameter, Δ(i,j) which indicates the degree of service needed by VOQ(i, j). This value is adjusted according to the input loading and the accumulation of cells experienced in previous service cycles. This dissertation also explores an analysis technique based on feedback control theory. This methodology is proposed to study the stability of arbitration and matching schemes for packet switches. A continuous system is used and a control model is used to emulate a queuing system. The technique is applied to a matching scheme. In addition, the study shows that the dwell time, which is defined as the time a queue receives service in a service opportunity, is a factor that affects the stability of a queuing system. This feedback control model is an alternative approach to evaluate the stability of arbitration and matching schemes