A study on the association between HSV-2 and HIV serostatus in HIV seropositive and HIV seronegative individuals

Background: HSV-2 is a common cause of genital ulcer disease worldwide and has become a prominent public health issue over recent years. The major public health importance of HSV-2 relates to its potential role in facilitating HIV transmission. This study was done to know the association of HSV-2 and HIV, to compare the seropositivity of HSV-2 in HIV seropositive and HIV seronegative individuals and also to study the influence of certain factors on HSV-2 infection.Methods: Blood samples collected from 132 HIV seropositive individuals and 50 HIV seronegative blood donors were screened for HSV-2 type specific IgG antibodies by ELISA.Results: 84 samples of the test group tested positive for HSV-2 (63.63%), which is significantly higher compared to 3 (6%) positives of the control group. HSV-2 seropositivity was significantly higher in individuals with multiple sexual partners, illiterates, daily wage workers.Conclusions: High seropositivity of HSV-2 in HIV seropositive individuals demonstrates the need for regular screening of HSV-2 in HIV seropositive individuals. It also clearly shows that education and occupation will influence the prevalence of HIV and HSV-2.

PINT: Probabilistic In-band Network Telemetry

© 2020 ACM. Commodity network devices support adding in-band telemetry measurements into data packets, enabling a wide range of applications, including network troubleshooting, congestion control, and path tracing. However, including such information on packets adds significant overhead that impacts both flow completion times and application-level performance. We introduce PINT, an in-band network telemetry framework that bounds the amount of information added to each packet. PINT encodes the requested data on multiple packets, allowing per-packet overhead limits that can be as low as one bit. We analyze PINT and prove performance bounds, including cases when multiple queries are running simultaneously. PINT is implemented in P4 and can be deployed on network devices.Using real topologies and traffic characteristics, we show that PINT concurrently enables applications such as congestion control, path tracing, and computing tail latencies, using only sixteen bits per packet, with performance comparable to the state of the art

BUILDING EFFICIENT AND RELIABLE SOFTWARE-DEFINED NETWORKS

Software-defined networking (SDN) promises flexible control of computer networks by orchestrating switches in the network dataplane through a centralized controller. However, despite this promise, operators used to fast and fault-tolerant routing using traditional protocols face three important problems while deploying SDN. One, the control plane timescales are too slow to enforce effective load balancing in order to efficiently use the available network capacity. Second, the commodity SDN switches have limited memory to enforce fine-grained policy rules which undermines the promise of flexible control. Third, the centralized controller itself is a single point of failure which is unacceptable for operators used to running distributed fault-tolerant network protocols. This thesis aims to mitigate these problems using novel algorithms that exploit advanced data plane capabilities and enhancements to the control plane software. At the same time, we also provide simple abstractions on top of these systems so that network operators writing control programs need not worry about low-level details of the underlying implementation mechanisms. First, we will present HULA, which gives the abstraction of one big efficient non-blocking switch. Instead of asking the control plane to choose the best path for each new flow, HULA efficiently routes traffic on least congested paths in the network. HULA uses advanced hardware data plane capabilities to infer global congestion information and uses that information to do fine-grained load balancing at RTT timescales. HULA is congestion-aware, scales to large topologies and is robust to topology failures. Second, we will present CacheFlow which helps enforcing fine-grained policies by proposing the abstraction of a switch with logically infinite rule space. CacheFlow uses a combination of software and hardware data paths to bring the best of both worlds to policy enforcement. By dynamically caching a small number of heavy hitting rules in the hardware switch and the rest of the rules in the software data path, it achieves both high throughput and high rule capacity. Since cross-rule dependencies make rule caching difficult, CacheFlow uses novel algorithms to do dependency-aware, efficient rule caching in a way transparent to control applications . Finally, we will present Ravana which gives the abstraction of one logically centralized controller. Given this abstraction, the network operator only writes programs for one controller and the Ravana runtime takes care of replicating the control logic for fault-tolerance. Since network switches carry additional state external to the controller state, Ravana uses an enhanced version of traditional replicated state machine protocols to ensure ordered and exactly-once execution of network events. Together these systems propose a new SDN paradigm where basic routing is done efficiently at dataplane timescales, policy enforcement is done scalably with the help of software data planes and the control plane is fault-tolerant. This new architecture has the properties of fast routing and fault-tolerance of traditional networks while delivering the promise of efficient enforcement of fine-grained control policies

DYNAMIC BASED IPTV SERVICES THROUGH VIRTUAL ENVIRONMENT IN CLOUD RESOURCES

Cloud computing is a new infrastructure environment that delivers on the promise of supporting on-demand services in a flexible manner by scheduling bandwidth, storage and compute resources on the fly. IPTV services like Video on Demand (VoD) and Live broadcast TV require substantial bandwidth and compute resources to meet the real time requirements and to handle the very bursty resource requirements for each of these services. To meet the needs of the bursts of requests, each with a deadline constraint for both VoD and Live TV channel changes, we propose a resource provisioning framework that allows these services to co-exist on a common infrastructure by taking advantage of virtualization. We propose an optimal algorithm that provides the minimum number of servers needed to fulfill all requests for these. We prove this optimality in a general setting for any number of services with general deadline constraints. By using real world data from an operational IPTV environment, our results show that anticipating and thereby enabling the delaying of VoD requests by up to 30 seconds gives significant resource savings even under conservative environmental assumptions. We also experiment with different scenarios (by varying the deadline constraints, changing the peak to average ratios of the constituent services) to compute the overall savings

Incremental consistent updates

A consistent update installs a new packet-forwarding policy across the switches of a software-defined network in place of an old policy. While doing so, such an update guarantees that every packet entering the network either obeys the old policy or the new one, but not some combination of the two. In this paper, we introduce new algorithms that trade the time required to perform a consistent update against the rule-space overhead required to implement it. We break an update in to k rounds that each transfer part of the traffic to the new configuration. The more rounds used, the slower the update, but the smaller the rule-space overhead. To ensure consistency, our algorithm analyzes the dependencies between rules in the old and new policies to determine which rules to add and remove on each round. In addition, we show how to optimize rule space used by representing the minimization problem as a mixed integer linear program. Moreover, to ensure the largest flows are moved first, while using rule space efficiently, we extend the mixed integer linear program with additional constraints. Our initial experiments show that a 6-round, optimized incremental update decreases rule space overhead from 100 % to less than 10%. Moreover, if we cap the maximum rule-space overhead at 5 % and assume the traffic flow volume follows Zipf’s law, we find that 80 % of the traffic may be transferred to the new policy in the first round and 99 % in the first 3 rounds

Infinite CacheFlow in software-defined networks

Software-Defined Networking (SDN) enables fine-grained poli-cies for firewalls, load balancers, routers, traffic monitoring, and other functionality. While Ternary Content Address-able Memory (TCAM) enables OpenFlow switches to pro-cess packets at high speed based on multiple header fields, today’s commodity switches support just thousands to tens of thousands of rules. To realize the potential of SDN on this hardware, we need efficient ways to support the abstraction of a switch with arbitrarily large rule tables. To do so, we de-fine a hardware-software hybrid switch design that relies on rule caching to provide large rule tables at low cost. Unlike traditional caching solutions, we neither cache individual rules (to respect rule dependencies) nor compress rules (to preserve the per-rule traffic counts). Instead we“splice ” long dependency chains to cache smaller groups of rules while preserving the semantics of the network policy. Our design satisfies four core criteria: (1) elasticity (combining the best of hardware and software switches), (2) transparency (faith-fully supporting native OpenFlow semantics, including traf-fic counters), (3) fine-grained rule caching (placing popular rules in the TCAM, despite dependencies on less-popular rules), and (4) adaptability (to enable incremental changes to the rule caching as the policy changes)